Early postoperative refractive outcomes of pediatric intraocular lens implantation
Bibliographic record
Abstract
PURPOSE: To evaluate the refractive outcome using 5 intraocular lens (IOL) calculation formulas to determine which best predicts refraction after pediatric cataract surgery. SETTING: The Hospital for Sick Children, Toronto, Ontario, Canada. METHODS: This study comprised a review of the charts of 158 consecutive patients aged 2 to 17 years old who were operated on by 1 of 2 staff surgeons between May 1992 and April 2000. The surgeons performed a total of 206 cataract extractions with primary or secondary IOL implantation. The measured outcome was the actual refraction 2 to 6 months postoperatively versus the target refraction. Two regression formulas (SRK, SRK II) and 3 theoretical formulas (Holladay 1, Hoffer Q, SRK/T) were used to predict refractive outcome based on preoperative axial length, corneal curvature, IOL power, and the IOL A-constant provided by the manufacturer. RESULTS: Forty-nine patients (59 IOL implantations) with available data 2 to 6 months after surgery were studied. Also analyzed were data from a subset of 31 patients (34 IOL implantations) with available data 2 to 3 months after surgery. There was poor to moderate agreement between the predicted and actual postoperative refractions using the SRK formula (intraclass correlation coefficient [ICC] = 0.50/0.04 [2- to 3-month follow-up/2- to 6-month follow-up]) and good or fair agreement using the other formulas (ICC from 0.60/0.24 for SRK II to 0.67/0.37 for Hoffer Q). The mean difference between the predicted and actual postoperative refractions with all formulas ranged from 1.06 to 1.22 diopters (D)/1.35 to 1.79 D (median 0.81 to 0.99 D/0.94 to 1.40 D; range 3.03 to 5.57 D/6.75 to 9.21 D). Using Holladay 1 and SRK, 9% to 18%/23% to 39% eyes were more than +/-2.00 D off the target outcome refraction. CONCLUSIONS: All 5 IOL power calculation formulas were unsatisfactory in achieving the target refraction. This finding may have implications for predicting long-term outcomes, interpreting previous reports of refractive outcomes, and obtaining preoperative informed consent in a clinical setting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".